Signal Timing Optimization Research Articles

Multiobjective optimization model of intersection signal timing considering emissions based on field data: A case study of Beijing

ABSTRACTMost existing signal timing models are aimed to minimize the total delay and stops at intersections, without considering environmental factors. This paper analyzes the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. First, considering the different operating modes of cruising, acceleration, deceleration, and idling, field data of emissions and Global Positioning System (GPS) are collected to estimate emission rates for heavy-duty and light-duty vehicles. Second, multiobjective signal timing optimization model is established based on a genetic algorithm to minimize delay, stops, and emissions. Finally, a case study is conducted in Beijing. Nine scenarios are designed considering different weights of emission and traffic efficiency. The results compared with those using Highway Capacity Manual (HCM) 2010 show that signal timing optimized by the model proposed in this paper can decrease vehicles delay and emissions more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.Implications: Vehicle emissions are heavily at signal intersections in urban area. The multiobjective signal timing optimization model is proposed considering the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. The results indicate that signal timing optimized by the model proposed in this paper can decrease vehicle emissions and delays more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.

Measuring the Influences and Impacts of Signalized Intersection Delay Reduction on the Fuel Consumption, Operation Cost and Exhaust Emissions

With the rapid urban expansion and economic development, vehicle fuel dissipation and exhaust emissions have been identified as major energy wastage and urban air pollutions in Kalar City and Iraqi Kurdistan Region in general. Traffic congestion is a growing problem in Kalar City and other urban areas. As it increases, the delay at the transportation network will increase. Any increase of the delay in the transportation network will reflect negatively by increasing the delay at the signalized intersections. Therefore, a study on delay and its relation to fuel consumption, operation cost and emissions at signalized intersection are necessary. This paper, studies the influences and impacts of signalized intersection delay reduction on the fuel consumption, operation cost, and exhaust emissions. A simulation is carried out to evaluate the existing conditions of selected intersections by estimating the intersection delay, operation cost, and emissions. The simulation results show that fuel consumption, operation cost, and emissions are high and directly proportional to the intersection delay. To reduce intersection delay, a signal timing optimization is carried out to the selected intersections. The optimization results show that the delay reduction has a significant influence and impacts in reducing; fuel wastage, operation cost, and exhaust emissions.

Microscopic Simulation and Optimization of Signal Timing based on Multi-Agent: A Case Study of the Intersection in Tianjin

The purpose of this paper is to optimize the signal timing through multi-agent simulation technology. Firstly, a conceptual model of the actual intersection is described. Secondly, the social dynamics model of the vehicle and pedestrian evolution rules are established from the micro perspective, which is simulated respectively by Anylogic and Synchro on this basis. Finally, the signal timing strategies for the different vehicle priorities are discovered through the heuristic algorithm. The case study shows that: ①The actual signal timing is not reasonable. ②The optimization strategies of signal timing can improve traffic efficiency. ③By comparing the signal timing strategies of different vehicle priority, the study shows that the Anylogic is more superior to the Synchro, which provides a new way to solve the traffic congestion.

Emission factor calibration and signal timing optimisation for isolated intersections

Signal control strategy has a huge effect not only on intersection operations but also on traffic emissions. The basic assumptions are first proposed. Using the second-by-second data of vehicular velocity and acceleration, the mathematical expressions are then presented to calibrate the emission factors during green/red on the basis of vehicle-specific power. Given some necessary constraints, a single-objective optimisation model and a bi-objective optimisation model are formulated with the concern of traffic emissions. Considering three levels of traffic demands and two methods of signal timing, the numerical examples are carried out by utilising the VISSIM and MATLAB software packages. The findings indicate that the emission factors during green are explicitly greater than those during red and they are all stable for each pollutant and for each lane group; a scientific signal control system can simultaneously reduce vehicle delay and traffic emissions for isolated intersections.

Real-Time Microscopic Traffic Simulation and Optimization at Intersections with Video Traffic Detection

In this paper, real-time vehicular data from video traffic detection (VTD) are used for minimizing the travel delay at intersections and a real-time traffic optimization model, based on the SUMO traffic simulation software, is established accordingly. The proposed model is implemented in a small industrial control computer which serves as the communication interface between the traffic signal control system, the traffic simulation and optimization model and the real-time video traffic detection equipment to optimize the respective signal timing plans. The decision to shorten or extend the respective green time can be made at each second under the current development. To verify the results of the simulation study a field study is undertaken in Hefei, China. The overall intersection performances before and after the signal timing optimization are calculated and compared. The results show that the method proposed in this paper can significantly minimize the vehicle delay and the corresponding air pollution in real time.

Optimal Signal Timing for Multi-Phase Intersections

We present an optimal signal timing policy for multi-phase intersections. The policy is optimal in the sense of minimizing the increase in the number of queuing vehicles, i.e. maximizing throughput, within one signal cycle. By introducing weights in the optimality criterion, our solution also accommodates the setting of prioritized traffic, in which it is more important for certain classes of traffic to wait for a shorter time. The proposed signal timing policy is simple to compute and implement. In particular, we show that the proposed strategy can be computed as a solution to a linear programming problem. Additionally, upon parametrizing the proposed policy with respect to the vehicle queue lengths, we analyze some aspects of the multi-cycle behavior of the closed-loop system generated by the policy, most notably convergence of the queue lengths over time. Finally, we illustrate the proposed control law with two numerical examples, one of which is based on real data collected from an existing complex multi-phase intersection.

Robust Optimization of Signal Control Parameters for Unsaturated Intersection Based on Tabu Search-Artificial Bee Colony Algorithm

In order to overcome the drawback of the conventional signal timing optimization, a robust optimization algorithm for signal control parameters based on Tabu search-artificial bee colony algorithm is proposed under unsaturated flow condition. Based on the analysis of the characteristics of traffic signal control, a robust optimization model of signal control parameters is constructed by considering the minimum the average delay and the mean square error of average delay. As a consequence, the formation process of the initial solution to the bee colony is improved and the robust optimization model is solved by using the Tabu search-artificial bee colony algorithm. The proposed robust optimization model is validated by using an intersection in Zhangye City of China. The simulation results have shown that the robust optimization model and the algorithm are feasible and practicable. This robust model and algorithm can effectively deal with the volatility of traffic flow and reduce traffic delays.

Signal Timing Optimization for Corridors with Multiple Highway-Rail Grade Crossings Using Genetic Algorithm

Safety and efficiency are two critical issues at highway-rail grade crossings (HRGCs) and their nearby intersections. Standard traffic signal optimization programs are not designed to work on roadway networks that contain multiple HRGCs, because their underlying assumption is that the roadway traffic is in a steady-state. During a train event, steady-state conditions do not occur. This is particularly true for corridors that experience high train traffic (e.g., over 2 trains per hour). In this situation, the non-steady-state conditions predominate. This paper develops a simulation-based methodology for optimizing traffic signal timing plan on corridors of this kind. The primary goal is to maximize safety, and the secondary goal is to minimize delay. A Genetic Algorithm (GA) was used as the optimization approach in the proposed methodology. A new transition preemption strategy for dual tracks (TPS_DT) and a train arrival prediction model were integrated in the proposed methodology. An urban road network with multiple HRGCs in Lincoln, NE, was used as the study network. The microsimulation model VISSIM was used for evaluation purposes and was calibrated to local traffic conditions. A sensitivity analysis with different train traffic scenarios was conducted. It was concluded that the methodology can significantly improve both the safety and efficiency of traffic corridors with HRGCs.

Optimal Signal Timing Method of Intersections Based on Bus Priority

According to the traffic problems such as traffic congestion and buses moving slowly, the paper based on the idea of bus priority to introduced the per capita minimum delay of signal control method. In this paper, the intersection of Huaguang Road and Liuquan Road in Zibo Zhangdian was used as an example to propose a bus signal timing plan. By considering the impact of traffic delays and designing the phase of the intersection. It can reduce the per capita delay and improve service levels. Using Trans Modeler to simulate the timing scheme, the results showed that the optimized scheme can effectively reduce the per capita delay and ensure priority access for buses.

A model and genetic algorithm for area-wide intersection signal optimization under user equilibrium traffic

The use of signal control systems to reduce traffic movement conflicts at intersections often affects operational efficiency of an urban street network. This study introduces a new method for area-wide traffic signal timing optimization under user equilibrium traffic. The optimization model is formulated as a multi-dimensional search problem aimed to achieve minimized product of the total travel time associated with urban street network and the variance of travel time for unit distance of travel. A genetic algorithm is developed to derive the model solution. A simulation control protocol embedded in PARAMICS software tool capable of conducting area-wide microsimulation is adopted to design the logic frame and function module of the area-wide traffic signal control system. Computational experiment is performed for model application on the Central Business District of Nanjing, China for validation. The results show that mobility improvements are achieved after applying the proposed model along with the genetic algorithm for area-wide signal timing optimization, assessed by extended capacity ratio, and reductions in through and turning movement delays, as well as average and variance of travel time for unit distance of travel.

A semi-analytic approach to model signal plans in urban corridors and its application in metaheuristic optimization

ABSTRACTThis article introduces a new formulation for modeling and evaluating the effects of traffic signal timing over arterial roads, based on the Lighthill Whitham Richards model. We introduce a Hamilton–Jacobi formulation of the traffic model, in which the red phases of the traffic signals are described as internal conditions corresponding to a zero flow (fixed bottlenecks). With this approach, it is possible to model and evaluate the traffic signal timings accurately and efficiently. We then integrate this fast simulation scheme into an optimization framework for computing the optimal signal timing strategy associated with a given set of initial and boundary conditions, over a transportation network. A numerical implementation is shown for a single link case and for a more complex signalized intersection.

A new nondominated sorting genetic algorithm based to the regression line For fuzzy traffic signal optimization problem

Traffic jam is a daily problem in nearly all major cities in the world and continues to increase with population and economic growth of urban areas. Traffic lights, as one of the key components at intersections, play an important role in control of traffic flow. Hence, study and research on phase synchronization and time optimization of the traffic lights could be an important step to avoid creating congestion and rejection queues in a urban network. Here, we describe the application of NSGA-II, a multi-objective evolutionary algorithm, to optimize both vehicle and pedestrian delays in an individual intersection. Results show that parameters found by improved NSGA-II can be superior to those defined by a traffic engineer with respect to several objectives, including total queue length of vehicles and pedestrians. In this paper, we improve NSGA-II algorithm based to the regression line to find a Pareto-optimal solution or a restrictive set of Pareto-optimal solutions based on our solution approaches to the problem, named PDNSGA (Non-dominated Sorting Genetic Algorithm based on Perpendicular Distance). In this paper, our purpose is to present a solution methodology to obtain all Pareto-optimal solutions to optimize traffic signal timing and enable the decision-makers to evaluate a greater number of alternative solutions. The proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain. Further jobs should be concerned on the signal timing optimization method for the oversaturated coordinated intersections or small-scale road network and real-field applications with the traffic signal controller. The high speed of the proposed algorithm and its quick convergence makes it desirable for large scheduling with a large number of phases. Furthermore, we have used the mean deviation from the ideal point (MDI) measure to compare the performance of the MOGA, PDNSGA, NSGA-II, and WBGA by the ANOVA method. It is demonstrated that the our proposed algorithm (PDNSGA) gives better outputs than those of MOGA, NSGA-II, and WBGA in traffic signal optimization problem, statistically .

Group-based hierarchical adaptive traffic-signal control part I: Formulation

A group-based adaptive traffic-control method for isolated signalized junctions is developed that includes a hierarchical structure comprising tactical and local levels of signal timing optimization. The control method optimizes the signal timings in adaptive traffic-control systems, and takes full advantage of flexible new technologies to incorporate the most up-to-date traffic information, as collected in real time. The definitions, combinations, and sequencing of the cycle structure stages are generated automatically using a procedure for optimizing the signal-timing plans in response to online data from traffic detectors. This new method provides a wider search space and improves the efficiency of the signal-control systems, thus improving the junction performance, minimizing delays, and maximizing capacity in real time. A multi-resolution strategy is proposed for updating the elements of the signal plans cycle-by-cycle and adjusting the current green signal timing second-by-second. The group-based variables and parameters for the proactive global-optimization method utilize lane-based predictive traffic-flow information, such as arrival and discharge rates, expressed as the slopes of polygonal delay formulas. Therefore, there is a high degree of flexibility in the tactical identification of the optimal signal plan in response to the real-time predicted traffic information, the objective function of the polygonal delay formula, and the direct differential equations for the adaptive group-based variables. The reactive local signal-control policy, which is formed based on the max-pressure strategy, is developed to locally adjust the current green signal time and to accommodate delicate demand fluctuations second-by-second at the fine-resolution level. The most appropriate cycle-structure for the tactical level of control is identified using a group-based global-optimization procedure that takes advantage of the latest available information. In part II of this study (Lee et al. 2017), the effectiveness of the proposed methods is validated based on the actualized mathematical frameworks, computer simulations, and a case study, using the appropriate computer programs.

Group-based hierarchical adaptive traffic-signal control Part II: Implementation

In part I of this study (Lee et al., 2017), the formulation of a theoretical framework for a group-based adaptive traffic-control method for isolated signalized junctions is presented, which includes tactical and local levels of signal timing optimization. The global level control aims to determine the time-varying cycle structure, with a resolution of cycles, and the real-time adjustment of the green phase, with a resolution of seconds, based on longer-term traffic information observed by traffic detectors. Overall, the purpose of the study is to actualize a multi-resolution strategy for a group-based adaptive signal-control method and establish a microscopic simulation platform to implement the proposed methodology and test its effectiveness. To actualize the global proactive-optimization scheme, in this paper, a rolling-horizon approach to the temporal and spatial variables, signal structures for four-arm intersections, and discrete directional search methods is applied using the developed mathematical framework. The formulation of the group-based max-pressure policy is realized using the logical form of the local reactive-control policy at a typical directional three-lane, four-arm approach to an isolated intersection. The integrated group-based adaptive traffic-signal control is actualized using VISSIM, Fortran, and VBA based on the developed tactical and local levels of signal timing optimization. The results of the computer simulations and the case study presented in this paper show that the integrated group-based adaptive traffic-signal-control logic outperforms the other methods over a wide range of traffic conditions, from free-flowing traffic to extreme congestion. Moreover, the proposed models perform much better than the existing fixed-signal plan and the actuated signal-control in asymmetric traffic conditions.

Revisiting the application of simultaneous perturbation stochastic approximation towards signal timing optimization

ABSTRACTSimultaneous Perturbation Stochastic Approximation (SPSA) has gained favor as an efficient optimization method for calibrating computationally intensive, “black box” traffic flow simulations. Few recent studies have investigated the efficiency of SPSA for traffic signal timing optimization. It is important for this to be investigated, because significant room for improvement exists in the area of signal optimization. Some signal timing methods and products perform optimization very quickly, but deliver mediocre solutions. Other methods and products deliver high-quality solutions, but at a very slow rate. When using commercialized desktop signal timing products, engineers are often forced to choose between speed and solution quality. Real-time adaptive control products, which must optimize timings within seconds on a cycle-by-cycle basis, have limited time to reach a high-quality solution. The existing literature indicates that SPSA provides the potential for upgrading both off-line and on-line solutions alike, by delivering high-quality solutions within seconds. This article describes an extensive set of optimization tests involving SPSA and genetic algorithms (GAs). The final results suggest that GA was slightly more efficient than SPSA. Moreover, the results suggest today's signal timing solutions could be improved significantly by incorporating GA, SPSA, and “playbooks” of preoptimized starting points. However, it may take another 5–10 years before our computers become fast enough to simultaneously optimize coordination settings (i.e., cycle length, phasing sequence, and offsets) at numerous intersections, using the most powerful heuristic methods, at speeds that are compatible with real-time adaptive solutions.

Traffic management model for vehicle re-routing and traffic light control based on Multi-Objective Particle Swarm Optimization

In this paper, a novel traffic management model is presented, which simultaneously optimizes vehicle re-routing and traffic light control to alleviate traffic congestion and limit the effects of incidents on traffic flow based on Multi-Objective Particle Swarm Optimization (MOPSO) method. Once a co ngested road is predicted, our proposed Multi-Objective Traffic Light Control is then applied to optimize signal timing which takes the maximization of traffic flow on the edge where the incident takes place and the minimization of the average junction waiting time as two objectives. To improve the performance and sensitivity of MOPSO algorithm, we used Q-Learning algorithm to grant to each agent of the swarm the ability of selecting appropriate MOPSO parameters adapted to the structure of the problem. At the same time, when the situation of the traffic flow starts to become more serious, we adopt a novel Multi-Objective Vehicle Re-routing strategy for assigning alternatives routes to cars before entering the congested road, in order to perform dynamic load balancing. Vehicle re-routing is also optimized by MOPSO to simultaneously find the shortest and least popular path. The obtained results from the simulation using SUMO, a well-known microscopic traffic simulator, confirm the efficiency of the proposed system.

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

AbstractThis article develops an efficient methodology to optimize the timing of signalized intersections in urban street networks. Our approach distributes a network‐level mixed‐integer linear program (MILP) to intersection level. This distribution significantly reduces the complexity of the MILP and makes it real‐time and scalable. We create coordination between MILPs to reduce the probability of finding locally optimal solutions. The formulation accounts for oversaturated conditions by using an appropriate objective function and explicit constraints on queue length. We develop a rolling‐horizon solution algorithm and apply it to several case‐study networks under various demand patterns. The objective function of the optimization program is to maximize intersection throughput. The comparison of the obtained solutions to an optimal solution found by a central optimization approach (whenever possible) shows a maximum of 1% gap on a number of performance measures over different conditions.

Modelling and Analysis of Oversaturated Intersections Using Jointly Hybrid Petri net and Hybrid Automata

The Urban traffic network is a typical example of a complex system called hybrid dynamic system. It is also a multivariable and multi-scale system having the interaction of two dynamics kinds. The difficulties encountered in the study of traffic and especially in the traffic signal control problem is how to model the traffic flow, how to define its variables and how to analyze their behavior. In this paper, we propose a modeling of oversaturated intersections traffic a view to designing a traffic control approach. This concept is based on the joint use of the two well-known hybrid representation tools: hybrid Petri networks and hybrid automata. The interest of this combination is to profit from the both models advantages while avoiding their disadvantages. A formal verification property is performed to refine this model. This technique is based on the computation of the reachable state spaces. Indeed, the new model captures important aspects of the traffic flow dynamics. Its favorable structure can be used in order to provide an efficient signal-timing plan to avoid oversaturation and to ease congestion. The numerical results show that the coordination traffic signal obtained by the proposed control approach outperforms those obtained using the widely utilized signal timing optimization software SYNCHRO under various demand scenarios from unsaturated to oversaturated.

Distributed coordinated signal timing optimization in connected transportation networks

This paper presents a Distributed-Coordinated methodology for signal timing optimization in connected urban street networks. The underlying assumption is that all vehicles and intersections are connected and intersections can share information with each other. The novelty of the work arises from reformulating the signal timing optimization problem from a central architecture, where all signal timing parameters are optimized in one mathematical program, to a decentralized approach, where a mathematical program controls the timing of only a single intersection. As a result of this distribution, the complexity of the problem is significantly reduced thus, the proposed approach is real-time and scalable. Furthermore, distributed mathematical programs continuously coordinate with each other to avoid finding locally optimal solutions and to move towards global optimality. We proposed a real-time and scalable solution technique to solve the problem and applied it to several case study networks under various demand patterns. The algorithm controlled queue length and maximized intersection throughput (between 1% and 5% increase compared to the actuated coordinated signals optimized in VISTRO) and reduced travel time (between 17% and 48% decrease compared to actuated coordinated signals) in all cases.

A Signal Timing Optimization in Traffic Management using ABC Algorithm

A Signal Timing Optimization in Traffic Management using ABC Algorithm